Impact of Audit Committee Quality on the Financial Performance of Conventional and Islamic Banks
Bibliographic record
Abstract
A lot of previous research studied the relationship between audit committee quality and the financial performance of conventional banks before and during the subprime crisis, whereas some other investigations analyzed the same association in the framework of Islamic banks. However, no study has compared these two correlations either before, during, or after the subprime crisis. Several reasons explain the differences, such as the audit committee quality of each bank type, the evaluation method of the financial performance, the research peculiarities, the methodology, the data, and the interpretation. This research aims to compare the impacts of the audit committees’ quality on the financial performance of Islamic and conventional banks between 2010 and 2019. The financial performance measures and audit committees’ determinants of the conventional and Islamic banks concerned 112 banks of each type. The collected data covered four continents: America, Asia, Africa, and Europe. Impacts were compared by using the Generalized Least Squares analysis. The results showed that the audit committee reduced the profitability of two bank types. Moreover, it harmed the conventional banks’ efficiency but reported an unclear effect within Islamic banks. Even so, we noticed that the audit committee had a positive impact on the conventional banks’ liquidity, while the same effect was apparently ambiguous for the Islamic banks’ liquidity. For solvency, the audit committee positively influenced conventional banks while it affected that of Islamic banks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".